mkofler96 / DeepSDFStruct / 27753924605

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Add homogenization (#65)

* Add test experiment for homogenization model with training artifacts

- Introduced new experiment directory for testing homogenization.
- Added latest model, optimizer parameters, latent codes, and logs as binary files.
- Created specs.json to define network architecture and training parameters.
- Added training summary in training_summary.json.
- Implemented a test function to train the homogenization model in test_train_model.py.

* Add homogenization network and update training loss calculation

* added xlim option back to plot sdf

* Update test experiment specs and data directory for homogenization model

* Refactor homogenization model tests to use shared data directory fixture

* set default dtype to float32 in test train model

* reduced test duration by reducing sampling points

* fix retry 429 error

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Fix negative Jacobian at mesh resolution 10 by filtering tets in float64

The orientation check ran in float32 but torchfem evaluates Jacobians in
float64 (after verts.to(float64)). Tets with a tiny positive float32
volume can flip negative in float64, causing the "Negative Jacobian"
exception. Compute the final validity mask in float64 so the filter is
consistent with torchfem.

* added HF secret

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